According to some estimates, video streaming traffic exceeds 50 percent of the total traffic over content distribution networks (CDNs). In adaptive streaming, when delivering media content to a client device, the client device may select appropriate segments dynamically based on a variety of factors, such as network conditions, device capability, and user choice. For example, the client device may select a segment with the highest quality (e.g., resolution or bit-rate) possible that can be downloaded in time for playback without causing stalling or rebuffering events in the playback. Thus, the client device may seamlessly adapt its media content playback to changing network conditions. One form of adaptive streaming is DASH (Dynamic Adaptive Streaming over Hypertext Transfer Protocol (HTTP)), which is designed to promote efficient delivery of multimedia content from servers to clients through HTTP-based content distribution networks
Video streams using DASH (or equivalents such as Apple HLS, Adobe HDS, etc.) exhibit some relatively specific properties. For example: video streams are long lived, ranging from a few minutes for some YOUTUBE™ clips to over an hour for some NETFLIX™ movies; video streams are typically described in a manifest at the onset of the connection, making it possible to know the semantics of the stream ahead of time, and; video streams are predictable in the sense that the sequence of packets is predetermined by the video stream's description and the network conditions. The consumption of video streams exhibits strong daily patterns, with a significant peak during“prime time” hours. From a network operator's perspective, not only will video streaming consume a lot of network resources, it will also require over-provisioning the network for a peak usage that can be much higher than the average. This results in a significant amount of unused capacity for a majority of network operation time.